Any ideas why the plot for the ridge regression isn't working correctly. If i plot this using the code below the first dotted line should show the optimal value for lambda. The graph doesn't seem to reflect the min value(optimal value) Any ideas?
x1 and y2 are matrix
ridge_cv_fit = cv.glmnet(x2, y2, alpha=0, standardize=FALSE, lambda=grid) plot(ridge_cv_fit) (lambda_ridge_min = ridge_cv_fit$lambda.min)